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Pubmed for Handhelds

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  • Title: Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate.
    Author: Zanetti R, Aminifar A, Atienza D.
    Journal: Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():4248-4251. PubMed ID: 33018934.
    Abstract:
    Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.
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